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Improving the CalEnviroScreen Score at the CA-Baja CA borderSan Diego-Tijuana Air Quality Task Force meetingTijuana, Baja California, September 26, 2018Penelope (Jenny) JE Quintana, PhD, MPH, San Diego State University
Javier Emmanuel Castillo Quiñones, PhD, Universidad Autónoma de Baja California, Tijuana
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▪ Science-based tool
▪ Uses 19 indicators of environmental, health and socioeconomic conditions
▪ Evaluates multiple pollution sources
▪ While accounting for a community’s vulnerability to pollution
▪ http://oehha.ca.gov/calenviroscreen/maps-data
▪ Border communities felt as if they had only 180 degrees of environmental exposures used for their scores, not 360 degrees.
▪ Purpose of this CARB (CA Air Resources Board) contract is to map emissions sources near border, model potential effects, and calculate new scores
2California Communities Environmental Health Screening Tool
Investigators:• San Diego State University: Graduate School of Public Health, Penelope Quintana (PI of Project) and
Zohir Chowdhury, have measured emissions from border crossings, currently also OEHHA-funded project to deploy community air sensing in the border community of San Ysidro, CA to input values to CalEnviroScreen.
• San Diego State University: Dept. of Geography, Atsushi Nara, Trent Biggs and Fernando De Salas, who provide vital mapping, GIS, and modeling expertise
• Molina Center for Energy and the Environment, Luisa Molina, Miguel Zavala and Victor Almanza of the Molina Center for Strategic Studies in Energy and the Environment, who are experts in measuring and modeling air pollution in Mexico, and who led the CAL-MEX 2010 air pollution campaign in Tijuana, Baja California
• UABC, Campus Tijuana: Dept of Chemistry and Chemical Engineering, Emmanuel Castillo Quiñones,, who has overseen student collection of air pollution data in Tijuana in past studies, and participated in Cal-Mex 2010
• UABC, Campus Mexicali: Dept of Geography, Judith Ley Garcia,, who has worked in social geography and environmental disparities
• James Sadd, Occidental College, Manuel Pastor, University of Southern California who developed the EJSM and who have a deep understanding of data needs and gaps in relation to utility for the EJSM and CES
• Lynn Russell, expert on regional atmospheric aerosols
• With advisory role: Dr. Margarito Quintero Núñez, Director de Planeación y Política Ambiental Secretaríade Protección al Ambiente
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Technical plan: task overview • Task 1. Locate, aggregate and characterize existing data on emission sources on
the Mexican side of the California-Baja California border, focusing on those sources that impact communities in California
• Task 1.1 Conduct planning meetings.
• Task 1.2 Compile existing data.
• Task 1.3. Create process to evaluate and edit data (online map validation and groundtruthing)
• EXPLORATORY AIM - Detection of burning activities in the border region such as agricultural burns in the Mexico area through automated detection using satellite images (MODIS)
• Task 2. Model-based identification of the influence of major sources
• Task 3. Evaluate impact on CalEnviroScreen (CES) and Environmental Justice Screening Method (EJSM) scores, and identify data gaps
• Task 4. Create and provide formatted data and detailed methodology in a format that can be incorporated into CalEnviroScreen and EJSM, provide final report
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MNEI (Mexico National Emissions Inventory) for Baja California 2013 -mappedMNEI 2014 Received from SEMARNAT in Jun 5th, 2018 • Extracted 78 relevant emission facilities by Molina Center (Dr. Miguel
Zavala)• 59 facilities match with MNEI 2013 records• 12 new emission facilities• Locations of 7 facilities cannot be validated
RETC (Registro de Emisiones y Transferencia de Contaminantes) for Baja California ( 2013, 2016 mapped, 2014, 2015 in progress)
Also 472 gas stations• 27 Landfills• 59 Brick Kilns• 46 Dry Cleaning• 15 Feedlots/Ranches• Major Railroad lines through Mexicali
Task 1.2 - Compile Existing Data8
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Task 1.3 Evaluate and edit dataData Validation Procedures
• Step-By-Step Instructions on Validation Procedures
SDSU Method - 8 Methods of Validation Based on:
• Lat/Long initial result
• Google Maps
• Google Earth
• Geocoding
• Google Search
• Business Name Search
• Call Business
• Call Current Business or Tenant
• Field Investigation (Ground Truthing)
Task 1.3 - Evaluate and Edit Data9
Groundtruthing (Direct observations in the field)• Validate both site location and emission characteristics by business
types• Emission facilities within a 1,500 m buffer from the border• Use mobile device field data collection tool, Arc Collector
Arc Collector
Reference: http://www.esri.com/software/arcgis/collector-for-arcgis
Web-GIS map is available at:• https://arcg.is/1yeSie
Key GIS layers: • Facility of Interest (FOI) Layer
All facility of interest collected in this projectAttribute fields for emission data include both MNEI 2013 and 2014
• FOI 1500m LayerFacility of interest within the 1.5 km buffer from the U.S.-Mexico borderAttribute fields for emission data include both MNEI 2013 and 2014
• FOI 1500m field validationA facility of interest layer used for groundtruthingContaining field notes and images taken in the field
10Task 1.2 - Compile Existing DataTask 1.3 - Evaluate and Edit Data
Task 1.2 - Compile Existing DataTask 1.3 - Evaluate and Edit Data
11• https://arcg.is/1yeSie
1/1/2017
1/1/2017
1/9/2017
1/25/2017
2/2/2017
3/6/2017
3/14/2017
3/22/2017
• An exploratory activity will be to have the SDSU Geography researchers detect burning activities such as agricultural burns in the Mexicali area.
• MODIS - Thermal Anomalies & Fire• Cell size: ~1,000m x ~1,000m
• Cycle: 1 day x 2 (Terra & Aqua)
MODIS – Fire Mask – 8 day composite From 1/1/2017 To 3/30/2017 (11 images)
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Task 1 - Exploratory Aim
11Find burning activities (e.g., agricultural burns) in the US-Mexico border area
Data Source: MODIS Fire Product• 1,545 HDF files from 2000 – 2018Time Series Analysis • Find relationships between fire activities and agricultural land-uses
# o
f fire
pix
els (
1km
x
1km
)
Monthly fire activities from March 2000 to February 2018
# o
f fire
eve
nts
Monthly fire activities from February 2000 to February 2018
Fire events
Task 1 - Exploratory Aim
12Find burning activities (e.g., agricultural burns) in the US-Mexico border area
Data Source: MODIS Fire Product• 1,545 HDF files from 2000 – 2018Time Series Analysis • Find relationships between fire activities and agricultural land-uses
2016 2017
Task 1 - Exploratory Aim
Fire Events
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Task 1 - Exploratory Aim
Mexicali
Tijuana
Find burning activities (e.g., agricultural burns) in the US-Mexico border area
Identify crop fields: Land Cover – 2010 (Landsat, 30m)
Not urban or Crop FieldCrop FieldUrban
http://www.cec.org/tools-and-resources/map-files/land-cover-2010-landsat-30m
Task 2: Drs. Luisa Molina, Miguel Zavala, Victor Almanza, Molina Center
• The database obtained from task 1 was used to identify the emission sources with the largest potential for impacting areas in San Diego, Calexico and adjacent US-Mexico border communities using a combination of short and long-term meteorological and air quality modeling simulations.
• A prerequisite to identify the influence of major emission sources impacting sensitive areas in the California-Mexico border is to understand the meteorological patterns that drive the dispersion, formation and fate of air pollutants in the region.
• Long-term time-series analysis of meteorological and air quality observations
• Simulation of the local (~ 1 km grids) and regional (~ 3 km grid) meteorology in the California-Mexico border using the WRF model.
• Application of backward trajectories techniques to identify potential emission locations influencing sensitive areas in the California-Mexico border region.
• Application of forward trajectories with FLEXPART-WRF aimed to prioritize the influence of specific potential emission sources.
Selection of events with southern wind component
San Diego – Tijuana: 21 episodes (28 days)Calexico – Mexicali: 35 episodes (77 days)
San Diego – Tijuana
Calexico – Mexicali
Forward dispersion, release points: Tijuana, Mexicali February 28 to March 1, 2014, 12 hrs
Backward trajectories
US
Mexico
San Ysidro
Task 3 –ongoing . Impact on CES and EJSM scores (James Sadd/Manuel Pastor –:• EJ Screening Method (EJSM): The EJSM includes hazard proximity in its exposure scoring. The measured distance between hazardous
facilities/uses and locations to be scored are used in a calculation of the hazard proximity metric. Currently, this metric does not reflect hazards proximate to border areas, but these new data will allow these to be included in calculating the hazard proximity metric, which will result in higher values for border census tracts. Because the EJSM Hazard Proximity Score only considers hazards with a 3000 ft distance, only those facilities and uses located within that distance of the international border will affect any change in the Score. These include:
• MNEI and RETC facilities
• Gas stations• Railroad land uses
• CalEnviroScreen (CES): CalEnviroScreen is Periodic updates of CalEnviroScreen include consideration and adoption of new data sources or improved metrics. Shortly after this project was approved, CES released a new version (CES3) in April of this year, and a newer version is being developed. This has required that we be flexible and forward looking in producing data useful to CES.
• · MNEI and RETC facilities: can be used to improve the data quality of the Toxic Releases Indicator by combining these facilities with TRI facilities in preparation of this Indicator (by outside vendor)
• · Landfills/Brick Kilns/Manufacturing facilities: can be used to improve the Environmental Effects component by incorporating them in a manner consistent with the way in which similar facilities within California are used. This depends on the ancillary information available for each facility, such as volume produced, number of employees, and facility size; the information available would determine exactly how each facility or class of facilities would be included.
• · Railroad land uses: there is potential for this data to inform the CES Diesel PM Indicator, but details have not been worked out until this data is complete.
• · MODIS fire detection information: there is potential for its use, but too little is known at this time to speculate how it might inform CES metrics or scoring.
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Thanks for your attention!
Contact:Penelope (Jenny) Quintana [email protected] Emmanuel Castillo Quiñones [email protected]
Mapping activities: Atsushi Nara [email protected] Ley Garcia [email protected]
Trajectory analysis:Luisa Molina [email protected]
Effect on environmental justice mapping scores:James Sadd [email protected]
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(EXTRA SLIDES)20
Task 4.
• Create and provide formatted data and detailed methodology in a format that can be incorporated into CalEnviroScreen and EJSM, and prepare and submit final report:
• Create a final geodatabase or shapefiles formatted for direct input to CES and EJSM programming, as well as full documentation of methods and procedures for data collection, validation and modeling.
• We will utilize both open-source and proprietary software resources to produce the final geodatabase including ArcGIS, PostgreSQL, PostGIS, and Python. Our data management framework is capable of flexibly converting one format to another in order to meet CES and EJSM data format requirements. The type of metadata that will be included is ISO19115-1, which defines the schema required for describing geographic information and services.
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Spatial Database
Transportation Network
Remote Sensing Imagery
Topographic
Data Management
Modules
Emissions Sources
Census Demographics
(Mexico)
Administrative Boundary
PostgreSQL&
PostGISPython
(& ArcGIS)
Input
…
KML/KMZ Metadata
CES Format
Geodatabase/Shapefile
Output
EJSMFormat GeoJSON Raster
Image
Data Management Framework
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Task 1.2 - Compile Existing DataTask 1.3 - Evaluate and Edit Data
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Facility of InterestWithin a 50km buffer from U.S.-Mexico border 1098
MNEI 558business/location validated 502business/location not valid or cannot be validated 56
Feedlot 16business/location validated 16business/location not valid or cannot be validated 0
Landfill 15business/location validated 14business/location not valid or cannot be validated 1
Gas Station 400business/location validated 396business/location not valid or cannot be validated 4
Brick Kilns 57business/location validated 55business/location not valid or cannot be validated 2
Dry Cleaning 52business/location validated 48business/location not valid or cannot be validated 4
Outside 50km buffer 125Total 1223
Facility of InterestWithin a 1.5km buffer from U.S.-Mexico border 209
MNEI 100business/location validated 74business/location not valid or cannot be validated 26
Feedlot 2business/location validated 0business/location not valid or cannot be validated 2
Landfill 2business/location validated 1business/location not valid or cannot be validated 1
Gas Station 73business/location validated 69business/location not valid or cannot be validated 4
Brick Kilns 5business/location validated 3business/location not valid or cannot be validated 2
Dry Cleaning 27business/location validated 23business/location not valid or cannot be validated 4
Total 209
All FOI within a 50 km buffer from the U.S.-Mexico Border(FOIs outside of the 1.5km buffer were validated only using remove validation methods)
All FOI within a 1.5 km buffer from the U.S.-Mexico Border(FOIs were validated using both remote validation and groudtruthing)
Task 2. DatasetsDatabase Data type Sampling
periodSampling frequency Spatial Extent
Air Quality System (AQS-EPA)
Air Quality and Meteorological Information System (AQMIS-CARB)
Criteria Pollutants: O3, SO2, CO, PM10, PM2.5, NOx 1980-2017 1 hour Southern California
Toxic Pollutants: HAPS, VOCs 1980-2017 1 hour Southern California
Meteorological parameters: P, T, RH, U, V 1980-2017 1 hour Southern California
National Data Bouy Center (NDBC) Meteorological parameters: P, T, RH, U, V 1980-2017 1 hour Southern California
NOAA's National Estuarine Research Reserve System's (NERRS)
Meteorological parameters: P, T, RH, U, V 1980-2017 1 hour Southern California
Servicio Meteorológico Nacional (SMN)
EMAs meteorological parameters: T, wind, RH, P 2000-2016 10 minutes Baja California
National Center for Atmospheric Research (NCAR)
Reanalysis 2014 6 hours Global
Monitoring stations
Potential Area of Influence (PAI) vs Potential Source Regions (PSR)
Trajectory Analysis
Identification of southern wind events
Potential Area of Influence
Potential Source Regions
- Forward trajectories- Event specific
(few episodes)
- Backward trajectories- Frequency analysis
(all episodes)
Task 2. Framework
California-Mexico Border Emission Sources Spatial
Database
Trajectory Analysis
Output Task 1
PAI and PSR
Long-term Air Quality Database
WRF modeling
Transport patterns
Output Task 2
Task 2 28WRF Modeling
• 1-way nesting
D1: 45 x 45 km, 116 x 85
D2: 15 x 15 km, 151 x 127
D3: 3 x 3 km, 161 x 136
D4: 1 x 1 km, 127 x 127 SD/ TIJUANAD5: 1 x 1 km, 103 x 103 Calexico / Mexicali
• 35 vertical layers
• 2014 NCEP FNL reanalysis nudging every 6 hrs
• YSU PBL scheme
• RRTMG radiation scheme
• Grell convective parametrization
• 36 hrs run/12 hrs spin up
• 15 min outputsD1
D2
D3
D4 D5
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ExposuresEnvironmental
EffectsSensitive
PopulationsSocioeconomic
Factors
Ozone PM 2.5
Diesel PM Pesticides
Toxic releases
from facilities
Traffic density
Drinking water quality
Cleanup sites Groundwater threats
Hazardous waste
Impaired water bodies
Solid Waste
Children and Elderly
Asthma ER visits
Low Birth Weight Infants
Educational attainment
Linguistic isolation
Poverty
Unemployment
Pollution Burden Population Characteristics